At least one stressful and dangerous issue facing epileptic patients is the possible random and sudden occurrence of seizure. One manner in which the impact of seizure may be reduced involves detecting precursors of an oncoming seizure and implementing treatment to suppress it. Partially and/or fully integrated systems have been developed that attempt to detect precursors of seizure onset electrically, as well as clinically, seconds before a seizure occurs, and to combine this detection with neurostimulation in an effort to suppress the seizure. In implementing such integrated detection and treatment systems, minimizing the detection delay (e.g., less than 2 s for real time suppression) while maintaining high detection rate is challenging. Previous attempts were limited by either a long latency or a low detection rate. For example, in one system 100% detection rate has been achievable with a poor latency of 13.5 s, which makes it unsuitable for a real-time application. Another existing system exhibits good latency (e.g., less than 2 s) using a Linear Support Vector Machine (LSVM) classifier, but the detection rate (e.g., 84.4%) was not high enough and the average false alarm rate (e.g., max. 14.7%) was too high for practical use due to an intermittent limit of the LSVM.
Although the following Detailed Description will proceed with reference being made to illustrative embodiments, many alternatives, modifications and variations thereof will be apparent to those skilled in the art.